Tweet volume alone may not be a reliable predictor, since a small group of users can produce a large amount of tweets. E.g., political campaign, promotion tweets

Some of the Twellow preferences are self declared

There is very strong correlation between the number of Twitter users/tweets from each state and the population of each state. Usually the Pearson&apos;s correlation coefficient between 0.9 to 1.0 indicates Very strong correlation.

Categorized by engagement degree: the high engagement users achieved better prediction results. It may be due to two reasons. (1) high engagement users posted more tweets. It is more reliable to make the prediction using more tweets. (2) more engaged users were more involved in the election event, and were more likely to vote.Categorized by tweet mode: the original tweet prone users achieved better prediction results. It might suggest the difficulty of identifying users&apos; voting intent from retweets.Categorized by content type: No significant difference is found between two groupsCategorized by political preference: the right-leaning user group achieved significantly better results than left-leaning group.

2.
There is a surge of interest in building systems that harness thepower of social data to predict election results. # of Facebook users Twitter users’ talking about each # of Facebook Positive/negative candidate; who is talking “likes” & Twitter opinions about about which candidate : “follower” each candidate age, gender, state Tweets from @BarackObama and Real time semantic @MittRomney organized analysis of topic,by engagement on Twitter opinion, emotion, and popularity about each candidate Are Twitter Users Equal in Predicting Elections? Lu Chen, Wenbo Wang, Amit Sheth 2

3.
One problem seems to be ignored: Are social media users equal in predicting elections? They may be from different countries and states. They may be have different political beliefs. They may be of different ages. They may engage in the elections in different ways and with different levels of involvement. …… They may be … different in predicting elections…? WHOSE opinion really matters? Are Twitter Users Equal in Predicting Elections? Lu Chen, Wenbo Wang, Amit Sheth 3

4.
o We Studied different groups of social media users who engage in the discussions of 2012 U.S. Republican Presidential Primaries, and compare the predictive power among these user groups.Data: Using Twitter Streaming API, we collected tweets that contain the words“gingrich”, “romney”, “ronpaul”, or “santorum” from 01/10/2012 to 03/05/2012 (SuperTuesday was 03/06/2012). The dataset comprises 6,008,062tweets from 933,343users. Are Twitter Users Equal in Predicting Elections? Lu Chen, Wenbo Wang, Amit Sheth 4

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1 More than half of the users posted only one tweet. Only 8% of the users posted more than 10 tweets.  A small group of users (0.23%) can produce a large amount of tweets (23.73%) – Is tweet volume a reliable predictor?2 The usage of hashtags and URLs reflects the users intent to attract peoples attention on the topic they discuss. The more engaged users show stronger such intent and are more involved in the election event. Are Twitter Users Equal in Predicting Elections? Lu Chen, Wenbo Wang, Amit Sheth 6

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According to users preference on generating their tweets, i.e., tweet mode, we classified the users as original tweet-dominant, original tweet-prone, balanced, retweet-prone and retweet-dominant. 3EngagementDegree  The original tweet-dominant group accounts for the biggest proportion of users in every user engagement group.  A significant number of users (34.71% of all the users) belong to the retweet -dominant group, whose voting intent might be more difficult to detect. Are Twitter Users Equal in Predicting Elections? Lu Chen, Wenbo Wang, Amit Sheth 7

8.
We use target-specific sentiment analysis techniques to classify each tweet as positive or negative – whether the expressed opinion about a specific candidate is positive or negative. The users are categorized based on whether they post more information or more opinion. 4EngagementDegree  More engaged users tend to post a mixture of content, with similar proportion of opinion and information, or larger proportion of information. Are Twitter Users Equal in Predicting Elections? Lu Chen, Wenbo Wang, Amit Sheth 8

9.
We collected a set of Twitter users with known political preference from Twellow(http://www.twellow.com/categories/politics). Based on the assumption that a user tendsto follow others who share the same political preference as his/hers, we identified theleft-leaning and right-leaning users utilizing their following/follower relations. Wetested this method using a datasets of 3341 users, and it showed an accuracy of 0.9243. 5  Right-leaning users were (as expected) more involved in republican primaries in several ways: more users, more tweets, more original tweets, higher usage of hashtags and URLs. Are Twitter Users Equal in Predicting Elections? Lu Chen, Wenbo Wang, Amit Sheth 9

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 We utilized the background knowledge from LinkedGeoData to identify thestates from user location information. If the users state could not be inferred from his/her location in the profile, weutilized the geographic locations of his/her tweets. A user was recognized as froma state if his/her tweets were from that state. 6 The Pearsons r for the correlation between the number of users/tweets and the population is 0.9459/0.9667 (p<.0001). Are Twitter Users Equal in Predicting Elections? Lu Chen, Wenbo Wang, Amit Sheth 10

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Predicting a Users Vote• Basic idea: for which candidate the user shows the most support – Frequent mentions The user More mentions, – Positive sentiment posted opinion higher score about cMore positive/less The usernegative opinions, mentioned c but higher score did not post Nm(c): the number of tweets mentioning the candidate c opinion about c Npos(c): the number of positive tweets about candidate c Nneg(c): the number of negative tweets about candidate c (0 < < 1): smoothing parameter (0 < < 1): discounting the score when the user does not express any opinion towards c. Are Twitter Users Equal in Predicting Elections? Lu Chen, Wenbo Wang, Amit Sheth 11

12.
Prediction ResultsWe examine the predictive power of different user groups in predicting theresults of Super Tuesday races in 10 states.To predict the election results in a state, we used only the collection ofusers who are identified from that state. We examined four time windows -- 7 days, 14 days, 28 days and 56 days prior to the primary day. In a specific time window, a users vote was assessed using only the set of tweets he/she created during this time. The results were evaluated in two ways: (1) the accuracy of predicting winners, and (2) the error rate between the predicted percentage of votes and the actual percentage of votes for each candidate. Are Twitter Users Equal in Predicting Elections? Lu Chen, Wenbo Wang, Amit Sheth 12

14.
Revealing the challenge of Retweets may not necessarily8 identifying the vote intent of “silent reflect users attitude. majority” The right-leaning user group provides the most accurate prediction result. In the best case (56-day time window), it correctly predict the winners in 8 out of 10 states with an average Prediction of user’s vote based on prediction error of 0.1. more opinion tweets is not necessarily more accurate than the To some extent, it demonstrates the prediction using more information importance of identifying likely voters tweets in electoral prediction. Are Twitter Users Equal in Predicting Elections? Lu Chen, Wenbo Wang, Amit Sheth 14

15.
Our findingsTwitter users are not “equal” in predicting elections! The likely voters’ opinions matter more. Some users’ opinions are more difficult to identify because of their lower levels of engagement or the implicit ways to express opinions. Are Twitter Users Equal in Predicting Elections? Lu Chen, Wenbo Wang, Amit Sheth 15

19.
2 Representative of the Target Population Polling Social Media Analysis About 95% of US homes can bereached by landline telephone andcell phone.  About 60% of American adults Sampling the target population use social networking sites.randomly. Difficult to do random sampling. Weighting the sample to census Limited demographic dataestimates for demographic (although with some work, can becharacteristics (gender, race, age, improved).educational attainment, andregion).[1] Can Social Media Be Used for Political Polling? http://www.radian6.com/blog/2012/07/can-social-media-be-used-for-political-polling/ Are Twitter Users Equal in Predicting Elections? Lu Chen, Wenbo Wang, Amit Sheth 19

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3 Accurate measure of opinions Polling Social Media Analysis Ask people what they think Who will you vote for?  Look at what people talk about and extract their opinions ……  Not as accurate as Polling Are Twitter Users Equal in Predicting Elections? Lu Chen, Wenbo Wang, Amit Sheth 20

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4 Timeliness Polling Social Media Analysis Not be able to track people’s opinion in real time What is happening now Are Twitter Users Equal in Predicting Elections? Lu Chen, Wenbo Wang, Amit Sheth 21

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Social Media Analysis – Promising but VeryChallenging  Extracting demographic  Increasing number of social information media users  Identifying the target population  Convenient and comfortable whose opinion matter, e.g. the way to express opinions likely voters in electoral prediction  The analysis can be done in real  Discriminate personal opinion time from the voice of mainstream media and political campaign  Lower cost  More accurate sentiment A great complement (if not analysis/opinion mining, substitute) for polling especially the identification of opinions about a specific object Are Twitter Users Equal in Predicting Elections? Lu Chen, Wenbo Wang, Amit Sheth 22

23.
Our Twitris+ System kept tracking people’s opinion on 2012 U.S.Presidential Election in real time and this is what we saw on the Election Day … Subjective Information Extraction, Lu Chen 23

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 A key innovation in sentiment analysis, employed in Twitris+, is topic specific sentimentanalysis -- to associate sentiment with an entity. The same sentiment phrases may beassigned different polarities associated with different entities.Twitris+ tracks sentiment trend about different entities, and identifies topics/events thatcontribute to sentiment changes. The result is updated every hour. Sentiment change about BarackObama Analysis can be performed at location (eg, by state) or issue Positive/negative topics based level (eg, that contribute to such economy, tax, Sentiment change about change social issues – Mitt Romney women, …) Individual tweets related to chosen topic Are Twitter Users Equal in Predicting Elections? Lu Chen, Wenbo Wang, Amit Sheth 26